Semi-supervised learning approaches for predicting South African political sentiment for local government elections
This work addresses political sentiment analysis for South African elections, providing insights into public opinion, but it is incremental as it applies existing semi-supervised techniques to a new dataset.
The study analyzed Twitter sentiment during South African local government elections using a semi-supervised graph-based method to classify tweets as negative or positive, finding that general sentiment was negative towards all four major parties, with the ANC receiving the worst negative sentiment at 65% negative tweets.
This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties. Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns cantered around corruption, incompetence and loadshedding.